Machine learning in risk assessment for microvascular head and neck surgery
Gabriele Monarchi1, Davide Buso2, Chiara Paolantonio3
1Department of Medicine, Section of Maxillo-Facial Surgery, University of Siena, Viale Bracci, Siena, 53100, Italy. gabriele.monarchi@gmail.com.
Summary
Machine learning (ML) enhances head and neck microvascular surgery by improving risk assessment and predicting outcomes. Challenges include data quality and interpretability, but ML integration promises better patient management and surgical precision.
Area of Science:
- Surgical Innovation
- Medical Informatics
- Biomedical Engineering
Background:
- Traditional risk assessment in microvascular surgery has limitations in capturing complex outcomes.
- Machine learning (ML) offers advanced analytical capabilities for surgical data.
- Optimizing patient management and surgical results is a key goal in head and neck procedures.
Purpose of the Study:
- To explore the potential of machine learning (ML) in head and neck microvascular surgery.
- To enhance risk stratification, outcome prediction, and decision support using ML.
- To improve functional and aesthetic results through data-driven surgical optimization.
Main Methods:
- Analysis of preoperative, intraoperative, and postoperative data using ML algorithms.
- Development of ML models for risk stratification and complication prediction.
- Implementation of ML-driven decision support systems for surgical planning.
Main Results:
- ML demonstrates potential in predicting complications like flap failure and infections.
- Outcome prediction models provide estimations of functional and cosmetic results.
- ML-assisted flap selection considers patient-specific anatomical factors.
Conclusions:
- ML adoption faces challenges: data quality, interpretability, and ethical concerns.
- Standardized datasets, interpretable models, and clinical integration are crucial for ML success.
- ML can revolutionize risk assessment in microvascular surgery, improving outcomes and precision.
